ABSTRACT Optimizing the quantitative training of our workforce workforce is essential for maintaining the nation?s scientific competitiveness and leadership in promoting biomedical research that will improve human health. It is well documented that the misuse of statistical methods is common in basic biomedical science research, even among papers published in high impact journals (1-3). These problems stem mainly from a limited understanding of statistics, suggesting that scientists need better statistical training. Our MARC Scholars are completing baccalaureate majors in different colleges and/or different departments within one college. While many departments at NMSU currently offer or require statistics training, these courses are unlikely to provide appropriate statistical preparation for basic scientists given the obvious differences in study designs between science disciplines. Providing our students with curriculum designed around sample sizes, study designs, and types of data that are frequently encountered in biomedical research is ideal. This application requests funds to enrich the scientific training of all MARC Scholars by increasing their conceptual understanding and skills needed to analyze data, assess the literature, improve the quality of statistical reporting and analysis in their respective fields of research, and develop strong communication skills with peers within and outside the scientific community. The Supplement Aims are to: 1) strengthen the students? understanding and application of a quantitative- based approach when analyzing and solving problems; and 2) formalize the professional development curriculum by implementing effective practices aimed at integrating the students into the research community and helping them make connections that will advance not only the science itself, but their careers in biomedical research. Students will conduct a hands-on data analysis workshop in Summer, Fall and Spring semesters in which students will learn to perform basic descriptive and inferential analyses using ?real data.? Data sets will be chosen to provide challenges like those students might encounter in working with their own data (e.g., data entry errors, missing values, outliers). Their acquired knowledge will be translated into their own research presentations and critical evaluations of peer reviewed literature during regularly held meetings throughout the academic year. These activities will be accompanied by core mentorship skills essential for academic success in graduate school. Assessment and evaluation of program practices and outcomes are integrated into the design and continual refinement of programmatic elements. Proposed supplemental activities are institutionalized through course offerings.